Socratic AI: An Adaptive Tutor for Clinical Case Based Learning^*

Clinical reasoning is a fundamental skill in medical education that requires intensive faculty resources and deliberate practice. Here, we present the design and implementation of a novel adaptive Socratic tutor powered by large language models (LLMs) that facilitates case-based learning for medical trainees. Our system takes input from any structure of a published clinical case to create interactive and adaptive clinical scenarios where learners engage in realistic patient encounters with real-time feedback on their reasoning process. As a proof of concept, we demonstrate its use with the NEJM clinical pathological case series. This paper describes the architecture, knowledge representation, and educational design principles incorporated into our system, which we are releasing as an open-source tool for medical education. Our work demonstrates how LLMs can promote high-quality precision education in clinical reasoning and provide a structured assessment of the strengths and weaknesses of the learner.